Establishment of Early Multi-Indicator Prediction Models of Moderately Severe Acute Pancreatitis and Severe Acute Pancreatitis

He, Shan-Shan; Li, Dan; He, Qi-Yong; Chen, Xiao-Ping; Lin, Yong-Xu; Yu, Yun-Wen; Chen, Feng-Lin; Ding, Jian

doi:https://doi.org/10.1155/2022/5142473

Gastroenterology Research and Practice

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Abstract Introduction Methods Results Discussion Conclusions Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 5142473 | https://doi.org/10.1155/2022/5142473

Establishment of Early Multi-Indicator Prediction Models of Moderately Severe Acute Pancreatitis and Severe Acute Pancreatitis

Shan-Shan He,¹Dan Li,²Qi-Yong He,³Xiao-Ping Chen,⁴Yong-Xu Lin,²Yun-Wen Yu,³Feng-Lin Chen,²and Jian Ding⁵

Academic Editor: Roberto Caronna

Received30 Nov 2021

Revised03 Mar 2022

Accepted14 Mar 2022

Published04 Apr 2022

Abstract

Background. It is critical to accurately identify patients with severe acute pancreatitis (SAP) and moderately SAP (MSAP) in a timely manner. The study was done to establish two early multi-indicator prediction models of MSAP and SAP. Methods. Clinical data of 469 patients with acute pancreatitis (AP) between 2015 and 2020, at the First Affiliated Hospital of Fujian Medical University, and between 2012 and 2020, at the Affiliated Union Hospital of Fujian Medical University, were retrospectively analyzed. The unweighted predictive score (unwScore) and weighted predictive score (wScore) for MSAP and SAP were derived using logistic regression analysis and were compared with four existing systems using receiver operating characteristic curves. Results. Seven prognostic indicators were selected for incorporation into models, including white blood cell count, lactate dehydrogenase, C-reactive protein, triglyceride, D-dimer, serum potassium, and serum calcium. The cut-offs of the unwScore and wScore for predicting severity were set as 3 points and 0.513 points, respectively. The unwScore () and wScore () were superior to the acute physiology and chronic health evaluation II score (), the bedside index for severity in AP score (), and the Ranson score () in predicting MSAP and SAP, which were equivalent to the modified computed tomography severity index score (). Conclusions. The unwScore and wScore have good predictive value for MSAP and SAP, which could provide a valuable clinical reference for management and treatment.

1. Introduction

Acute pancreatitis (AP) refers to intrapancreatic trypsinogen activation caused by gallstones, hypertriglyceridemia, and alcohol abuse, followed by local pancreatic inflammation as the major clinical feature, with or without dysfunction of other organs [1, 2]. AP is the most common pancreatic disease worldwide [3]. Global incidence and mortality are estimated at 33.74 cases (95% confidence interval (CI): 23.33–48.81) per 100 000 person-years and 1.60 deaths (95% CI: 0.85–1.58) per 100,000 person-years [3]. The incidence appears to be rising continuously at an annual rate of 3.4% [4–7]. Although approximately 80–85% of patients with AP experience a mild disease course and have a good prognosis (self-limited, %), 15–20% develop moderately severe AP (MSAP) or severe AP (SAP) [4, 8–11], which have mortality rates as high as 30% [8, 12, 13]. Therefore, it is critical to accurately identify patients with SAP and MSAP in a timely manner.

In 1974, Ranson et al. [14] assessed the severity of AP for the first time. Since then, a series of clinical scoring systems have been gradually developed to stratify the severity and predict the prognosis of AP [15–17], including the Ranson score, the Glasgow score, the acute physiology and chronic health evaluation (APACHE) II score, the bedside index for severity in AP (BISAP), and the modified computed tomography severity index (MCTSI) [9, 17–19]. Nevertheless, both the Ranson score and the Glasgow score require 48 h to be determined. Therefore, the risk stratification cannot be performed at admission, leading to missing a potentially valuable and early therapeutic window. Furthermore, some parameters used in the Ranson score are not routinely collected at the time of hospitalization [9, 20, 21]. The APACHE II score has complex parameters, tedious operation, and poor predictive value within 24 h of onset [9, 21–24]. Wu et al. [21] pointed out that in their validation cohort, only 2.2% of cases had complete APACHE II data. Moreover, whether the APACHE II score reflects the presence of tissue necrosis remains a matter of debate [6]. Unfortunately, pancreatic parenchyma necrosis may not occur in the early stage of the disease, which restricts the early assessment of the severity of AP by MCTSI [10]. In contrast, the BISAP score, which can well predict the severity of AP, organ failure (OF), and death, is easier to calculate and can be evaluated by routine clinical data within 24 h [9, 21]. However, it has also been reported that its sensitivity to mortality and SAP is not ideal [25].

Early prediction of the severity of AP remains a huge challenge. In view of the shortcomings of current risk stratification tools, the purpose of this study is to explore the prognostic factors related to the severity of AP and to establish early multi-indicator prediction models, in order to provide a reference for clinical diagnosis and treatment of AP, early intervention, and hierarchical management.

2. Methods

2.1. Object of Study

Clinical data of 469 patients who met the inclusion criteria between January 1st, 2015, and June 30th, 2020, at the First Affiliated Hospital of Fujian Medical University, and between January 1st, 2012, and October 31st, 2020, at the Affiliated Union Hospital of Fujian Medical University, were retrospectively collected and analyzed, including 202 cases of mild AP (MAP), 240 cases of MSAP, and 27 cases of SAP.

2.2. Diagnostic Criteria and the Revised Atlanta Classification of AP [26]

According to the 2012 revision of the Atlanta classification and definitions by international consensus, the diagnosis of AP requires two of the following three features: (i) abdominal pain consistent with AP (acute onset of a persistent, severe, epigastric pain often radiating to the back); (ii) serum lipase activity (or amylase activity) at least three times greater than the upper limit of normal; and (iii) characteristic findings of AP on contrast-enhanced CT and less commonly magnetic resonance imaging or transabdominal ultrasonography. (1)MAP: MAP is characterized by the absence of local or systemic complications and OF.(2)MSAP: MSAP is characterized by OF that resolves within 48 h (transient OF) and/or local or systemic complications without persistent OF.(3)SAP: SAP is characterized by persistent OF (>48 h), including single OF and multiple OF.

OF is defined as a modified for the renal, respiratory, and/or cardiovascular system. Local complications are AP fluid collection, pancreatic pseudocyst, acute necrotic collection, walled-off necrosis, gastric outlet dysfunction, splenic and portal vein thrombosis, and colonic necrosis. Systemic complications are systemic inflammatory response syndrome (SIRS), OF, sepsis, intra-abdominal hypertension, abdominal compartment syndrome, and pancreatic encephalopathy.

SIRS criteria are as follows: (1) or <36°C, (2) breaths/minute or mmHg, (3) beats/minute, and (4) cells/mm³ or >12,000 cells/mm³ or >10% immature bands. SIRS is defined as the presence of 2 or more SIRS criteria.

2.3. Inclusion and Exclusion Criteria

Inclusion criteria were as follows: (1) patients who met the diagnostic criteria of AP, and had complete clinical data, (2) patients who were more than 18 years old, (3) patients in whom systemic examination and abdominal CT scanning were completed, and (4) initial onset.

Exclusion criteria were as follows: (1) patients who were less than 18 years old, (2) acute attack or recurrence of chronic pancreatitis, (3) pregnant or lactating patients, (4) AP caused by malignant tumor or abdominal space occupying lesion, (5) patients who had severe mental/neurological disorders or lack of self-awareness, and (6) whose clinical data were incomplete.

2.4. Research Indicators

The statistical data collected in this study include (1) the general clinical data of the patients, including sex, age, etiological type, vital signs, severity score, basic diseases, hospitalization days, and hospitalization expenses and (2) clinical indexes, including WBC, hemoglobin (HB), hematocrit (HCT), platelet (PLT), red blood cell volume distribution width (RDW), RDW/PLT, glucose (GLU), total bilirubin (TBIL), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), albumin (ALB), blood urea nitrogen (BUN), serum creatinine (sCr), the BUN/sCr ratio, total cholesterol (TCHOL), procalcitonin (PCT), bicarbonate ion (HCO₃⁻), triglyceride (TG), C-reactive protein (CRP), D-dimer, serum calcium, corrected calcium, serum phosphorus, serum sodium, and serum potassium.

2.5. Outcome Measurements

Secondary outcome measurements are as follows: the patient data were subjected to univariate analysis and binary logistic regression analysis to obtain independent prognostic indicators of the severity of AP.

Primary outcome measurements are as follows: using the above indicators, unweighted predictive score (unwScore) and weighted predictive score (wScore) for MSAP and SAP were established. The receiver operator characteristic (ROC) curves of independent prognostic factors, 4 existing systems, and prediction models were produced. The area under the curves (AUC) were compared while the cutoff values, the sensitivity, the specificity, the positive predictive values (PPV), the negative predictive values (NPV), and the accuracy rates were calculated to verify the predictive efficiency of new models.

2.6. Statistical Analysis

SPSS 24 software and MedCalc v19.3.0 software were used for statistical analysis, and GraphPad Prism 8 was used for drawing. The chi-square test or the Fisher exact test was used for countable data. Normally distributed data are expressed as the n (), and the independent sample -test was used for the comparison between groups. Nonnormally distributed data are expressed as the median (quartile spacing), and the Mann–Whitney rank sum test was used for comparison between groups. Binary logistic regression analysis was used to analyze the influencing factors. was considered to indicate statistical significance. ROC curves were used to determine the best critical value of each prognostic index and prediction model and their prediction values.

3. Results

3.1. Results of Univariate Analysis of Influencing Factors in the MAP and MSAP+SAP Groups

3.1.1. Comparison of General Data between the MAP and MSAP+SAP Groups

There was no significant difference in sex, vital signs (systolic blood pressure, diastolic blood pressure, and mean arterial pressure), APACHE II score, and hypertension between the two groups. There was a significant difference in age, etiology, vital signs (temperature, pulse rate, and respiratory rate), severity score (BISAP score, MCTSI score, and Ranson score), underlying diseases (diabetes, fatty liver, and hyperlipidemia), hospitalization days, and hospitalization expenses () (Table 1).

3.1.2. Comparison of Clinical Indexes between the MAP and MSAP+SAP Groups

There was no significant difference in PLT, RDW/PLT, TBIL, BUN, sCr, and BUN/sCr between the two groups (). There was a significant difference in WBC, HB, HCT, RDW, GLU, AST, LDH, ALB, TCHOL, TG, D-dimer, HCO₃⁻, CRP, PCT, serum calcium, corrected calcium, serum phosphorus, serum sodium, and serum potassium () (Table 2).

3.2. Multivariate Analysis of Independent Prognostic Factors in Patients with AP

The clinical indexes WBC, HB, HCT, PLT, RDW, RDW/PLT, GLU, TBIL, AST, LDH, ALB, BUN, sCr, BUN/sCr, TCHOL, TG, D-dimer, HCO₃⁻, CRP, PCT, serum calcium, corrected calcium, serum phosphorus, serum sodium, and serum potassium were included in the binary logistic regression analysis. The results showed that WBC, LDH, CRP, sCr, TG, D-dimer, and serum potassium were independent prognostic risk factors for MSAP and SAP, while serum calcium was an independent prognostic protective factor for MSAP and SAP (Table 3).

4. Establishment of Multi-Index Joint Prediction Models

4.1. Determination of the Critical Values of Independent Prognostic Indicators

The data were divided into the MAP group and the MSAP+SAP group, and the critical values of independent prognostic indexes for predicting MSAP+SAP were calculated on the basis of ROC curves. The results showed that WBC, LDH, CRP, TG, D-dimer, serum potassium, and serum calcium had predictive value for MSAP+SAP (Table 4, Figures 1 and 2), which were included in the unwScore model and the wScore model.

4.2. unwScore Model

According to the critical value of each index, the continuous variables were transformed into binary variables: /L was defined as 1, WBC ≤11.49 × 10⁹/L as 0; U/L was defined as 1, U/L as 0; mg/L was defined as 1, mg/L as 0; mM was defined as 1, mM as 0; mg/L was defined as 1, mg/L as 0; mM was defined as 1, mM as 0; and mM was defined as 1, mM as 0.

On the basis of the critical value of each index, the risk score of MSAP+SAP in patients with AP was calculated according to Equation (1). The critical value of the model for predicting the occurrence of MSAP and SAP in patients with AP was 3. With the increasing model score, the probability of occurrence of MSAP and SAP increased. The probabilities of occurrence of MSAP+SAP corresponding to risk scores of 0, 1, 2, 3, 4, 5, 6, and 7 were 8.82%, 7.14%, 36.11%, 53.47%, 80.95%, 87.14%, 97.50%, and 100%, respectively.

The unwScore model was superior to the APACHE II score, the BISAP score, and the Ranson score in predicting the occurrence of MSAP and SAP in patients with AP, which was similar to that of MCTSI (Table 5, Figure 3).

4.3. wScore Model

Defining MSAP and SAP as 1 and MAP as 0, binary logistic regression analysis was performed on WBC, LDH, CRP, TG, D-dimer, serum potassium, and serum calcium. The following regression equation (Equation (2)) was established (Table 6). The likelihood ratio test showed that the model was statistically significant (, ), while the Hosmer–Lemeshow test indicated that the goodness of fit was good (, ).

The wScore model was superior to the APACHE II score, the BISAP score, and the Ranson score in predicting the occurrence of MSAP+SAP in patients with AP, which was similar to the MCTSI score and the unwScore model (Table 5, Figure 3).

5. Discussion

AP is an inflammatory disease of highly variable severity, ranging from mild cases with low mortality to severe cases with high mortality [27]. Early severity stratification becomes critical, especially on the day of admission, as this period is regarded as a window of opportunity to prevent pancreatic necrosis and OF [9]. Currently, there are four frequently used AP scoring systems to assist clinicians to identify SAP and MSAP, namely, the APACHE II score, the Ranson score, the BISAP score, and the MCTSI score. Each scoring system has specific applications and advantages, but each also has limitations.

In the present study, we constructed early multi-indicator prediction models that comprise only seven prognostic indexes: WBC, LDH, CRP, TG, D-dimer, serum potassium, and serum calcium. Compared with the traditional scoring systems, the advantages of the unwScore and wScore models constructed in this study lie in their simplicity, safety, objectivity, low cost, early risk stratification, high predictive value, dynamic monitoring, and quantitative prediction.

In 1974, Ranson et al. [14] first put forward the Ranson score, which is a milestone in the assessment of AP severity. However, the Ranson score is based on 11 parameters identified as important prognostic factors, requiring 48 hours for a complete evaluation [28]. Similarly, it is only applicable within 48 hours of admission, and it is difficult to track and evaluate the changes of the disease in real time [29]. Different from the Ranson score, the unwScore and wScore models could be used simultaneously by using only seven variables within 24 hours after admission. Similarly, the unwScore model could also quantitatively predict the occurrence probability of MSAP and SAP. With the increasing model score, the probability of occurrence of MSAP and SAP increased.

In 2004, Mortele et al. [30] revised and simplified the CTSI score and put forward the MCTSI score, which correlated more closely with the severity of AP and the following parameters: the length of the hospital stay, the need for surgical or percutaneous procedures, and the occurrence of infection. However, the ideal time for CT scans is at least 72 hours after the onset of symptoms [31], and it is not recommended to conduct routine CT scans on admission purely for the purpose of severity assessment [32]. Enhanced CT scans are also not recommended when there is significant renal damage (usually creatinine levels higher than 1.5 mg/dL) or a history of obvious allergy to contrast media [33]. Compared with the MCTSI score, the advantages of our models are that they are not only simple and fast but also does not need imaging examination, thus reducing unnecessary radiation and economic burden. Especially when severe and unstable patients are not suitable for CT enhanced scanning, it is not conducive to the early evaluation of MCTSI score.

The APACHE score was originally designed to assess the severity of acute illness patients who entered the ICU in the 1970s [9]. In 1985, Knaus et al. [22] simplified the APACHE score and proposed the APACHE II score, which is the most commonly used. Still, the APACHE II score takes into account various parameters, including acute physiological variables, age, and chronic health conditions, some of which may not be relevant to AP prognosis, whereas other important measures, such as pancreatic injury and significant regional complications, are missed [21, 33, 34]. Unlike the APACHE II score, the seven prognostic indicators included in the unwScore and wScore models all play a key role in the occurrence and development of AP or have clinical significance [9, 20, 27, 35–52]. These indexes are easy to obtain in the clinic, and the detection method is convenient. Furthermore, the model algorithm is simple, rapid, feasible, and easy to popularize and monitor dynamically and is also suitable for grass-roots hospitals. Beyond that, the unwScore and wScore models can also avoid unnecessary pain and financial expenditure to patients because blood gas analysis is not necessary.

In 2008, Wu et al. [21] proposed the BISAP score. The detail of “SIRS” in the system is mainly based on the values of vital signs, which makes the score unstable so that evaluation has to be conducted repeatedly. And similar to APACHE II score, the criterion of “impaired mental status” in this system is subjective. Fortunately, all the variables included in the unwScore and wScore models are objective routine clinical indicators, thus reducing the subjective bias of evaluators, which could be used between different centers.

Compared to the four existing scoring systems, the performance of new models was good for the prediction of AP severity. The overall sensitivity, specificity, PPV, and NPV for the prediction of disease severity with the Ranson score were 75%, 77%, 49%, and 91%, respectively, according to a meta-analysis. At admission, the sensitivity of the APACHE II to predict SAP is 65%, with a specificity of 76%, a PPV of 43%, a NPV of 89%, and an accuracy of 75%. At 48 hours, the sensitivity of the APACHE II to predict SAP is 76%, with a specificity of 84%, a PPV of 54%, a NPV of 93%, and an accuracy of 70–80% [28, 53]. Though the NPVs of our scoring systems were not satisfactory (66.92% and 67.48%), they had similar sensitivity (67.42% and 70.41%) and accuracy (75.91% and 75.05%) and slightly higher specificity (87.13% and 81.19%) and PPV (87.38% and 83.19%). Moreover, compared with the traditional score, although the BISAP score is a big step forward in simplicity, it is not an advancement in accuracy [29]. A prospective study by Papachristou et al. [34] showed that the BISAP score (, 95% CI: 0.74–0.87) was similar to the Ranson score (, 95% CI: 0.89–0.97), the APACHE II score (, 95% CI: 0.71–0.84), and the MCTSI score (, 95% CI: 0.76–0.89) in predicting SAP. While preserving their advantages of simplicity and efficiency, the unwScore () and wScore () models constructed in this study could still predict the severity of AP well, and their predictive values were even superior to those of the BISAP score (), the APACHE II score (), and the Ranson score () and equivalent to that of the MCTSI score ().

In summary, we have derived two new scoring systems for predicting severity of AP based on commonly used clinical indexes. All seven variables included in this scoring system can be easily measured within 24 hours of admission. Compared to four existing scoring systems, the new prediction models are accurate in predicting disease severity (MSAP and SAP), which plays important roles in early evaluation, progress analysis, treatment plan adjustment, and prognosis judgment of AP.

6. Conclusions

WBC, LDH, CRP, sCr, TG, D-dimer, and serum potassium are independent prognostic risk factors for the severity of AP, while serum calcium is an independent prognostic protective factor. The early multi-indicator prediction models of MSAP and SAP have a good predictive efficiency and could provide a valuable clinical reference for prediction and treatment.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Authors’ Contributions

Shan-Shan He and Dan Li contributed equally to this work.

Acknowledgments

This work was supported by the Fujian Natural Science Foundation (grant number 2018J01314), Joint Funds for the Innovation of Science and Technology, Fujian Province (grant number 2017Y9048), Platform and Industry-University Collaboration Projects of Quanzhou, Fujian Province (grant number 2020N089s), Natural Science Foundation of Fujian Province (grant number 2019J01050912), and Educational Research Project for Young and Middle-aged Teachers of Fujian Province (grant number JK2017021). We thank LetPub (http://www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

References

M. S. Petrov and D. Yadav, “Global epidemiology and holistic prevention of pancreatitis,” Nature Reviews. Gastroenterology & Hepatology, vol. 16, no. 3, pp. 175–184, 2019.
View at: Publisher Site | Google Scholar
S. D. Crockett, S. Wani, T. B. Gardner et al., “American Gastroenterological Association Institute guideline on initial management of acute pancreatitis,” Gastroenterology, vol. 154, no. 4, pp. 1096–1101, 2018.
View at: Publisher Site | Google Scholar
A. Y. Xiao, M. L. Y. Tan, L. M. Wu et al., “Global incidence and mortality of pancreatic diseases: a systematic review, meta-analysis, and meta-regression of population-based cohort studies,” The Lancet Gastroenterology & Hepatology, vol. 1, no. 1, pp. 45–55, 2016.
View at: Publisher Site | Google Scholar
S. M. van Dijk, N. D. L. Hallensleben, H. C. van Santvoort et al., “Acute pancreatitis: recent advances through randomised trials,” Gut, vol. 66, no. 11, pp. 2024–2032, 2017.
View at: Publisher Site | Google Scholar
S. E. Roberts, S. Morrison-Rees, A. John, J. G. Williams, T. H. Brown, and D. G. Samuel, “The incidence and aetiology of acute pancreatitis across Europe,” Pancreatology, vol. 17, no. 2, pp. 155–165, 2017.
View at: Publisher Site | Google Scholar
C. Leal and N. Almeida, “Predicting severity in acute pancreatitis: a never-ending quest…,” GE Portuguese Journal of Gastroenterology, vol. 26, no. 4, pp. 232–234, 2019.
View at: Publisher Site | Google Scholar
P. J. Lee and G. I. Papachristou, “New insights into acute pancreatitis,” Nature Reviews. Gastroenterology & Hepatology, vol. 16, no. 8, pp. 479–496, 2019.
View at: Publisher Site | Google Scholar
G. Trikudanathan, D. R. J. Wolbrink, H. C. van Santvoort, S. Mallery, M. Freeman, and M. G. Besselink, “Current concepts in severe acute and necrotizing pancreatitis: an evidence- based approach,” Gastroenterology, vol. 156, no. 7, pp. 1994–2007.e3, 2019.
View at: Publisher Site | Google Scholar
P. Silva-Vaz, A. M. Abrantes, M. Castelo-Branco, A. Gouveia, M. F. Botelho, and J. G. Tralhão, “Multifactorial scores and biomarkers of prognosis of acute pancreatitis: applications to research and practice,” International Journal of Molecular Sciences, vol. 21, no. 1, p. 338, 2020.
View at: Publisher Site | Google Scholar
A. Leppäniemi, M. Tolonen, A. Tarasconi et al., “2019 WSES guidelines for the management of severe acute pancreatitis,” World Journal of Emergency Surgery : WJES, vol. 14, no. 1, p. 27, 2019.
View at: Publisher Site | Google Scholar
S. Tenner, J. Baillie, J. DeWitt, S. S. Vege, and American College of Gastroenterology, “American College of Gastroenterology guideline: management of acute pancreatitis,” The American Journal of Gastroenterology, vol. 108, no. 9, pp. 1400–1415, 2013.
View at: Publisher Site | Google Scholar
J. W. Tan, X. Q. Zhang, C. M. Geng, and L. L. Peng, “Development of the national early warning score-calcium model for predicting adverse outcomes in patients with acute pancreatitis,” Journal of Emergency Nursing, vol. 46, no. 2, pp. 171–179, 2020.
View at: Publisher Site | Google Scholar
C. D. Johnson and M. Abu-Hilal, “Persistent organ failure during the first week as a marker of fatal outcome in acute pancreatitis,” Gut, vol. 53, no. 9, pp. 1340–1344, 2004.
View at: Publisher Site | Google Scholar
J. H. Ranson, K. M. Rifkind, D. F. Roses, S. D. Fink, K. Eng, and F. C. Spencer, “Prognostic signs and the role of operative management in acute pancreatitis,” Surgery, Gynecology & Obstetrics, vol. 139, no. 1, pp. 69–81, 1974.
View at: Google Scholar
H. Zhou, X. Mei, X. He, T. Lan, and S. Guo, “Severity stratification and prognostic prediction of patients with acute pancreatitis at early phase,” Medicine (Baltimore), vol. 98, no. 16, article e15275, 2019.
View at: Publisher Site | Google Scholar
E. Zerem, “Treatment of severe acute pancreatitis and its complications,” World Journal of Gastroenterology, vol. 20, no. 38, pp. 13879–13892, 2014.
View at: Publisher Site | Google Scholar
Y. S. Tee, H. Y. Fang, I. M. Kuo, Y. S. Lin, S. F. Huang, and M. C. Yu, “Serial evaluation of the SOFA score is reliable for predicting mortality in acute severe pancreatitis,” Medicine (Baltimore), vol. 97, no. 7, article e9654, 2018.
View at: Publisher Site | Google Scholar
D. Juneja, P. B. Gopal, and M. Ravula, “Scoring systems in acute pancreatitis: which one to use in intensive care units?” Journal of Critical Care, vol. 25, no. 2, pp. 358.e9–358.e15, 2010.
View at: Publisher Site | Google Scholar
J. H. Cho, T. N. Kim, H. H. Chung, and K. H. Kim, “Comparison of scoring systems in predicting the severity of acute pancreatitis,” World Journal of Gastroenterology, vol. 21, no. 8, pp. 2387–2394, 2015.
View at: Publisher Site | Google Scholar
K. Schütte and P. Malfertheiner, “Markers for predicting severity and progression of acute pancreatitis,” Best Practice & Research. Clinical Gastroenterology, vol. 22, no. 1, pp. 75–90, 2008.
View at: Publisher Site | Google Scholar
B. U. Wu, R. S. Johannes, X. Sun, Y. Tabak, D. L. Conwell, and P. A. Banks, “The early prediction of mortality in acute pancreatitis: a large population-based study,” Gut, vol. 57, no. 12, pp. 1698–1703, 2008.
View at: Publisher Site | Google Scholar
W. A. Knaus, E. A. Draper, D. P. Wagner, and J. E. Zimmerman, “Severity of disease classification system. APACHE II,” Critical Care Medicine, vol. 13, no. 10, pp. 818–829, 1985.
View at: Publisher Site | Google Scholar
S. Al-Hadeedi, S. T. Fan, and D. Leaper, “APACHE-II score for assessment and monitoring of acute pancreatitis,” The Lancet, vol. 2, no. 8665, p. 738, 1989.
View at: Publisher Site | Google Scholar
L. Wang, Y. B. Zeng, J. Y. Chen et al., “A simple new scoring system for predicting the mortality of severe acute pancreatitis,” Medicine (Baltimore), vol. 99, no. 23, 2020.
View at: Publisher Site | Google Scholar
W. Gao, H. X. Yang, and C. E. Ma, “The value of BISAP score for predicting mortality and severity in acute pancreatitis: a systematic review and meta-analysis,” PLoS One, vol. 10, no. 6, 2015.
View at: Publisher Site | Google Scholar
P. A. Banks, T. L. Bollen, C. Dervenis et al., “Classification of acute pancreatitis—2012: revision of the Atlanta classification and definitions by international consensus,” Gut, vol. 62, no. 1, pp. 102–111, 2013.
View at: Publisher Site | Google Scholar
S. M. Staubli, D. Oertli, and C. A. Nebiker, “Laboratory markers predicting severity of acute pancreatitis,” Critical Reviews in Clinical Laboratory Sciences, vol. 52, no. 6, pp. 273–283, 2015.
View at: Publisher Site | Google Scholar
E. J. Balthazar, “Acute pancreatitis: assessment of severity with clinical and CT evaluation,” Radiology, vol. 223, no. 3, pp. 603–613, 2002.
View at: Publisher Site | Google Scholar
S. Chauhan and C. E. Forsmark, “The difficulty in predicting outcome in acute pancreatitis,” The American Journal of Gastroenterology, vol. 105, no. 2, pp. 443–445, 2010.
View at: Publisher Site | Google Scholar
K. J. Mortele, W. Wiesner, L. Intriere et al., “A modified CT severity index for evaluating acute pancreatitis: improved correlation with patient outcome,” American Journal of Roentgenology, vol. 183, no. 5, pp. 1261–1265, 2004.
View at: Publisher Site | Google Scholar
B. Sahu, P. Abbey, R. Anand, A. Kumar, S. Tomer, and E. Malik, “Severity assessment of acute pancreatitis using CT severity index and modified CT severity index: correlation with clinical outcomes and severity grading as per the Revised Atlanta Classification,” Indian Journal of Radiology and Imaging, vol. 27, no. 2, pp. 152–160, 2017.
View at: Publisher Site | Google Scholar
T. L. Bollen, V. K. Singh, R. Maurer et al., “A comparative evaluation of radiologic and clinical scoring systems in the early prediction of severity in acute pancreatitis,” The American Journal of Gastroenterology, vol. 107, no. 4, pp. 612–619, 2012.
View at: Publisher Site | Google Scholar
P. A. Banks, “Practice guidelines in acute pancreatitis,” The American Journal of Gastroenterology, vol. 92, no. 3, pp. 377–386, 1997.
View at: Google Scholar
G. I. Papachristou, V. Muddana, D. Yadav et al., “Comparison of BISAP, Ranson's, APACHE-II, and CTSI scores in predicting organ failure, complications, and mortality in acute pancreatitis,” The American Journal of Gastroenterology, vol. 105, no. 2, 2010.
View at: Publisher Site | Google Scholar
C. Dervenis, C. D. Johnson, C. Bassi et al., “Diagnosis, objective assessment of severity, and management of acute pancreatitis,” International Journal of Pancreatology, vol. 25, no. 3, pp. 195–210, 1999.
View at: Publisher Site | Google Scholar
J. Werner, W. Hartwig, W. Uhl, C. Müller, and M. W. Büchler, “Useful markers for predicting severity and monitoring progression of acute pancreatitis,” Pancreatology, vol. 3, no. 2, pp. 115–127, 2003.
View at: Publisher Site | Google Scholar
T. W. Frick, “The role of calcium in acute pancreatitis,” Surgery, vol. 152, no. 3, pp. S157–S163, 2012.
View at: Publisher Site | Google Scholar
K. Yang, Y. X. Ding, and W. C. Chin, “K⁺-induced ion-exchanges trigger trypsin activation in pancreas acinar zymogen granules,” Archives of Biochemistry and Biophysics, vol. 459, no. 2, pp. 256–263, 2007.
View at: Publisher Site | Google Scholar
R. Mukherjee, O. A. Mareninova, I. V. Odinokova et al., “Mechanism of mitochondrial permeability transition pore induction and damage in the pancreas: inhibition prevents acute pancreatitis by protecting production of ATP,” Gut, vol. 65, no. 8, pp. 1333–1346, 2016.
View at: Publisher Site | Google Scholar
S. Feng, Q. Wei, Q. Hu et al., “Research progress on the relationship between acute pancreatitis and calcium overload in acinar cells,” Digestive Diseases and Sciences, vol. 64, no. 1, pp. 25–38, 2019.
View at: Publisher Site | Google Scholar
J. Huai, Y. Shao, X. Sun, Y. Jin, J. Wu, and Z. Huang, “Melatonin ameliorates acute necrotizing pancreatitis by the regulation of cytosolic Ca²⁺ homeostasis,” Pancreatology, vol. 12, no. 3, pp. 257–263, 2012.
View at: Publisher Site | Google Scholar
H. Rinderknecht, “Fatal pancreatitis, a consequence of excessive leukocyte stimulation?” International Journal of Pancreatology, vol. 3, no. 2–3, pp. 105–112, 1988.
View at: Publisher Site | Google Scholar
Z. Dambrauskas, A. Gulbinas, J. Pundzius, and G. Barauskas, “Value of the different prognostic systems and biological markers for predicting severity and progression of acute pancreatitis,” Scandinavian Journal of Gastroenterology, vol. 45, no. 7–8, pp. 959–970, 2010.
View at: Publisher Site | Google Scholar
S. Yu, D. Wu, K. Jin et al., “Low serum ionized calcium, elevated high-sensitivity C-reactive protein, neutrophil-lymphocyte ratio, and body mass index (BMI) are risk factors for severe acute pancreatitis in patients with hypertriglyceridemia pancreatitis,” Medical Science Monitor, vol. 25, pp. 6097–6103, 2019.
View at: Publisher Site | Google Scholar
C. Gomercic, E. Gelsi, D. Van Gysel et al., “Assessment of D-dimers for the early prediction of complications in acute pancreatitis,” Pancreas, vol. 45, no. 7, pp. 980–985, 2016.
View at: Publisher Site | Google Scholar
C. Feistritzer and C. J. Wiedermann, “Effects of anticoagulant strategies on activation of inflammation and coagulation,” Expert Opinion on Biological Therapy, vol. 7, no. 6, pp. 855–870, 2007.
View at: Publisher Site | Google Scholar
A. Tripodi and P. M. Mannucci, “Markers of activated coagulation and their usefulness in the clinical laboratory,” Clinical Chemistry, vol. 42, no. 5, pp. 664–669, 1996.
View at: Publisher Site | Google Scholar
T. Salomone, P. Tosi, G. Palareti et al., “Coagulative disorders in human acute pancreatitis: role for the D-dimer,” Pancreas, vol. 26, no. 2, pp. 111–116, 2003.
View at: Publisher Site | Google Scholar
N. Ewald, P. D. Hardt, and H. U. Kloer, “Severe hypertriglyceridemia and pancreatitis: presentation and management,” Current Opinion in Lipidology, vol. 20, no. 6, pp. 497–504, 2009.
View at: Publisher Site | Google Scholar
H. Nawaz, E. Koutroumpakis, J. Easler et al., “Elevated serum triglycerides are independently associated with persistent organ failure in acute pancreatitis,” The American Journal of Gastroenterology, vol. 110, no. 10, pp. 1497–1503, 2015.
View at: Publisher Site | Google Scholar
J. Cui, J. Xiong, Y. Zhang et al., “Serum lactate dehydrogenase is predictive of persistent organ failure in acute pancreatitis,” Journal of Critical Care, vol. 41, pp. 161–165, 2017.
View at: Publisher Site | Google Scholar
O. Komolafe, S. P. Pereira, B. R. Davidson, K. S. Gurusamy, and Cochrane Upper GI and Pancreatic Diseases Group, “Serum C-reactive protein, procalcitonin, and lactate dehydrogenase for the diagnosis of pancreatic necrosis,” Cochrane Database of Systematic Reviews, vol. 2017, no. 4, 2017.
View at: Publisher Site | Google Scholar
C. E. Forsmark, J. Baillie, AGA Institute Clinical Practice and Economics Committee, and AGA Institute Governing Board, “AGA institute technical review on acute pancreatitis,” Gastroenterology, vol. 132, no. 5, pp. 2022–2044, 2007.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Shan-Shan He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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